qqkrls 1.1.1

Quantile-on-Quantile Kernel-Based Regularized Least Squares — A Python implementation of KRLS (Hainmueller & Hazlett, 2014) and QQKRLS (Adebayo et al., 2024) with publication-quality MATLAB-style visualizations.

QQKRLS — Quantile-on-Quantile Kernel-Based Regularized Least Squares

A production-grade Python library implementing KRLS (Kernel Regularized Least Squares), Quantile-on-Quantile Regression, and the combined QQKRLS estimator with publication-quality MATLAB-style visualizations and comprehensive diagnostic tests for academic research.

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Table of Contents

1. Overview
2. Theoretical Background
   • Kernel Regularized Least Squares (KRLS)
   • Quantile-on-Quantile Regression (QQ)
   • QQKRLS: The Combined Estimator
3. Installation
4. Quick Start
5. API Reference
   • Core Estimation
   • Visualization Functions
   • Diagnostic Functions
   • Table & Export Functions
6. Complete Workflow Guide
   • Step 1: Data Preparation & Descriptive Statistics
   • Step 2: Pre-Estimation Diagnostics
   • Step 3: KRLS Estimation
   • Step 4: QQKRLS Estimation
   • Step 5: Multi-Variable QQKRLS
   • Step 6: Visualization
   • Step 7: Export & Reporting
7. Ported From
8. References
9. Author
10. License

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Overview

QQKRLS combines three powerful econometric methodologies into a single, unified Python toolkit:

Component	Method	Purpose
KRLS	Kernel Regularized Least Squares	Nonparametric regression using Gaussian kernels with Tikhonov regularization — no linearity or additivity assumptions
QQ	Quantile-on-Quantile Regression	Distributional analysis examining how quantiles of X affect quantiles of Y
QQKRLS	Combined Estimator	Nonparametric marginal effects across all quantile pairs (θ, τ), capturing heterogeneous relationships at different distributional locations

Why QQKRLS?

Traditional regression methods assume:

• Linearity: the effect of X on Y is constant across all values.
• Homogeneity: the effect is the same at the mean, the tails, and everywhere in between.

QQKRLS relaxes both assumptions simultaneously, allowing researchers to discover:

• Nonlinear effects that vary across the distribution of X (θ-dimension).
• Heterogeneous impacts at different quantiles of Y (τ-dimension).
• Complex interaction patterns invisible to OLS, quantile regression, or standard KRLS alone.

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Theoretical Background

1. Kernel Regularized Least Squares (KRLS)

Reference: Hainmueller, J. & Hazlett, C. (2014). Political Analysis, 22(2), 143-168.

KRLS is a machine-learning regression method that finds a function $f$ in a reproducing kernel Hilbert space (RKHS) that minimises the penalised loss:

$$\hat{f} = \arg\min_{f \in \mathcal{H}K} \sum{i=1}^{N} (y_i - f(x_i))^2 + \lambda |f|_{\mathcal{H}_K}^2$$

By the representer theorem, the solution has the form:

$$\hat{f}(x) = \sum_{i=1}^{N} c_i \cdot K(x, x_i)$$

where $K$ is a Gaussian kernel:

$$K(x_i, x_j) = \exp!\left(-\frac{|x_i - x_j|^2}{\sigma}\right)$$

The coefficient vector $c^*$ is obtained via eigendecomposition:

$$c^* = V (\Lambda + \lambda I)^{-1} V^\top y$$

where $K = V \Lambda V^\top$ is the eigendecomposition of the kernel matrix.

Marginal Effects

Pointwise marginal effects for predictor dimension $d$ are computed analytically:

$$\frac{\partial \hat{f}(x_j)}{\partial x_j^{(d)}} = \frac{-2}{\sigma} \sum_{i=1}^{N} c_i \cdot K(x_j, x_i) \cdot (x_i^{(d)} - x_j^{(d)})$$

The average marginal effect is:

$$\overline{\text{ME}}d = \frac{1}{N} \sum{j=1}^{N} \frac{\partial \hat{f}(x_j)}{\partial x_j^{(d)}}$$

Bandwidth & Regularization

Parameter	Symbol	Selection Method
Bandwidth	$\sigma$	Default = number of predictors $D$
Regularization	$\lambda$	Leave-One-Out (LOO) cross-validation via golden-section search

The LOO error is computed efficiently using the "shortcut" formula:

$$\text{LOO} = \sum_{i=1}^{N} \left(\frac{c_i}{G^{-1}_{ii}}\right)^2, \quad \text{where } G^{-1} = V (\Lambda + \lambda I)^{-1} V^\top$$

2. Quantile-on-Quantile Regression (QQ)

Reference: Sim, N. & Zhou, H. (2015). Journal of Banking & Finance, 55, 1-12.

Quantile-on-Quantile regression examines how the $\theta$-th quantile of the independent variable $X$ affects the $\tau$-th conditional quantile of the dependent variable $Y$. This creates a two-dimensional mapping $\beta(\theta, \tau)$ that captures distributional heterogeneity.

The QQ approach:

1. For each $\theta \in (0, 1)$: compute $Q_X(\theta)$ and subset data where $X \leq Q_X(\theta)$.
2. For each $\tau \in (0, 1)$: estimate the $\tau$-th conditional quantile of $Y$ given the subset.
3. The coefficient $\beta(\theta, \tau)$ describes the marginal effect at quantile pair $(\theta, \tau)$.

3. QQKRLS: The Combined Estimator

Reference: Adebayo, T.S., Ozkan, O. & Eweade, B.S. (2024). Journal of Cleaner Production, 440, 140832.

QQKRLS synthesises KRLS and QQ regression:

Algorithm:

1. For each X-quantile $\theta$:

   • Compute the $\theta$-th quantile threshold: $q_\theta = Q_X(\theta)$
   • Subset data: ${(x_i, y_i) : x_i \leq q_\theta}$
   • Fit KRLS on the subset to obtain pointwise marginal effects $\left{\frac{\partial \hat{f}}{partial x}\right}$

2. For each Y-quantile $\tau$:

   • The coefficient is the $\tau$-th quantile of the marginal effects distribution: $$\beta(\theta, \tau) = Q_\tau!\left(\left{\frac{\partial \hat{f}{[\theta]}(x_j)}{\partial x_j}\right}{j=1}^{n_\theta}\right)$$

3. Statistical inference via bootstrap:

   • Resample with replacement $B$ times within each subset.
   • Compute bootstrap standard errors, $t$-statistics, and $p$-values.

The result is a heatmap matrix $\beta(\theta, \tau)$ showing how the nonparametric marginal effect varies across both distributions.

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Installation

From PyPI

pip install qqkrls

With full dependencies (interactive plots, diagnostics)

pip install qqkrls[full]

From source (development)

git clone https://github.com/merwanroudane/qqkrls.git
cd qqkrls
pip install -e .

Dependencies

Package	Minimum Version	Purpose
numpy	≥ 1.20	Core numerical computation
pandas	≥ 1.3	Data manipulation and results storage
scipy	≥ 1.7	Kernel distances, statistical tests
matplotlib	≥ 3.5	Publication-quality visualizations

Optional:

Package	Purpose
plotly	Interactive 3D surface plots
seaborn	Enhanced statistical plots
statsmodels	Extended diagnostic tests

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Quick Start

KRLS — Kernel Regularized Least Squares

import numpy as np
from qqkrls import krls, plot_krls_derivatives, plot_krls_fit

# Generate nonlinear data
np.random.seed(42)
n = 200
X = np.random.randn(n, 2)
y = np.sin(X[:, 0]) + 0.5 * X[:, 1]**2 + np.random.randn(n) * 0.3

# Fit KRLS
fit = krls(X, y, col_names=["x1", "x2"])

# Print summary
fit.summary()

# Visualize marginal effects
plot_krls_derivatives(fit, var_idx=0, title="Marginal Effect of x1")
plot_krls_fit(fit)

QQKRLS — Quantile-on-Quantile KRLS

import numpy as np
from qqkrls import qqkrls, plot_qqkrls_heatmap, plot_qqkrls_3d

# Generate data
np.random.seed(42)
x = np.random.randn(200)
y = 0.5 * np.sin(x) + np.random.randn(200) * 0.3

# Run QQKRLS
result = qqkrls(y, x, n_boot=100)

# Print summary
result.summary()

# Publication-quality heatmap (Adebayo et al. style)
plot_qqkrls_heatmap(result, title="QQKRLS: X → Y", colorscale="paper")

# 3D surface (MATLAB-style)
plot_qqkrls_3d(result, title="QQKRLS Surface", colorscale="jet")

# Export LaTeX table
latex = result.export_latex(caption="QQKRLS Coefficients")
print(latex)

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API Reference

Core Estimation

krls(X, y, ...)

Fit Kernel Regularized Least Squares.

from qqkrls import krls

fit = krls(
    X,                     # (N, D) predictor matrix
    y,                     # (N,) outcome vector
    kernel="gaussian",     # Kernel type: 'gaussian', 'linear', 'poly2', 'poly3', 'poly4'
    lambda_=None,          # Regularization parameter (None = LOO cross-validation)
    sigma=None,            # Gaussian kernel bandwidth (None = D)
    derivative=True,       # Compute pointwise marginal effects
    binary=True,           # Compute first differences for binary predictors
    vcov=True,             # Compute variance-covariance matrices
    eigtrunc=None,         # Eigenvalue truncation threshold (0, 1]
    col_names=None,        # List of predictor names
    verbose=1,             # 0=silent, 1=summary, 2+=detailed
)

Returns: KRLSResult with attributes:

Attribute	Shape	Description
coeffs	(N, 1)	Solution vector $c^*$
fitted	(N, 1)	Fitted values $\hat{y}$
X, y	(N, D), (N, 1)	Original data
K	(N, N)	Kernel matrix
sigma, lambda_	scalar	Bandwidth and regularization parameters
R2, Looe	scalar	R-squared and LOO error
derivatives	(N, D)	Pointwise marginal effects
avg_derivatives	(1, D)	Average marginal effects
var_avg_derivatives	(1, D)	Variance of average marginal effects
vcov_c	(N, N)	Variance-covariance of coefficients
vcov_fitted	(N, N)	Variance-covariance of fitted values

Methods:

• fit.summary() — Print publication-quality summary.
• fit.predict(newdata) — Predict for new observations.

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qqkrls(y, x, ...)

Run Quantile-on-Quantile Kernel-Based Regularized Least Squares.

from qqkrls import qqkrls

result = qqkrls(
    y,                     # Dependent variable
    x,                     # Independent variable
    y_quantiles=None,      # Quantiles of Y (τ). Default: 0.05, 0.10, ..., 0.95
    x_quantiles=None,      # Quantiles of X (θ). Default: same as y_quantiles
    sigma=None,            # Gaussian kernel bandwidth (None = auto)
    lambda_=None,          # Regularization parameter (None = LOO CV per subset)
    min_obs=15,            # Minimum observations in a subset to fit KRLS
    n_boot=200,            # Bootstrap replications for standard errors
    verbose=True,          # Print progress
)

Returns: QQKRLSResult with attributes:

Attribute	Type	Description
results	DataFrame	Long-format table: y_quantile, x_quantile, coefficient, std_error, t_value, p_value, significance
y_quantiles	ndarray	Y-quantile grid
x_quantiles	ndarray	X-quantile grid
n_obs	int	Number of observations

Methods:

• result.summary() — Print summary statistics.
• result.to_matrix(value) — Pivot to (τ × θ) matrix.
• result.significance_matrix(alpha) — Binary significance matrix.
• result.stars_matrix() — Matrix of significance stars.
• result.to_dataframe() — Full results DataFrame.
• result.export_csv(path) — Export to CSV.
• result.export_latex(...) — Export to LaTeX table.

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multi_qqkrls(y, X, ...)

Run QQKRLS for each column of X against y (multi-variable panel).

from qqkrls import multi_qqkrls

results_dict = multi_qqkrls(
    y, X,
    col_names=["GDP", "CO2", "Energy"],
    n_boot=200,
    verbose=True,
)
# Returns: dict mapping {var_name: QQKRLSResult}

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Visualization Functions

All plots follow MATLAB-style aesthetics for publication in top academic journals.

Function	Description	Key Parameters
plot_qqkrls_heatmap(result, ...)	Paper-style coefficient heatmap with significance stars	colorscale, show_stars, show_values, vmin, vmax
plot_qqkrls_3d(result, ...)	3D surface of QQKRLS coefficients (MATLAB-style)	elev, azim, colorscale
plot_qqkrls_contour(result, ...)	Filled contour plot of coefficients	levels, colorscale
plot_qqkrls_pvalue(result, ...)	P-value heatmap with significance regions	alpha
plot_qqkrls_panel(results_dict, ...)	Multi-variable panel of heatmaps	dep_var, colorscale
plot_krls_derivatives(fit, ...)	Pointwise marginal effects scatter + density	var_idx
plot_krls_fit(fit, ...)	Actual vs fitted scatter (45° line)	—
plot_krls_panel(fit, ...)	Panel of marginal-effect scatters for all predictors	—

Available Colorscales

Name	Description
"paper"	Red–White–Green diverging (Adebayo et al. 2024 style) — default
"paper_warm"	Red–White warm gradient
"jet"	Classic MATLAB Jet
"rdylgn"	Red–Yellow–Green diverging
"coolwarm"	Blue–Red diverging
"viridis"	Perceptually uniform
"plasma"	Perceptually uniform warm

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Diagnostic Functions

Pre-Estimation Diagnostics

from qqkrls import linearity_test_battery, print_diagnostics

# Run battery of tests to justify using KRLS/QQKRLS
diag_df = linearity_test_battery(y, X, col_names=["x1", "x2"])
print_diagnostics(diag_df)

Tests included:

• Ramsey RESET — Functional form misspecification
• Breusch-Pagan — Heteroskedasticity
• Jarque-Bera — Non-normality of residuals
• BDS — Nonlinear dependence in residuals

Post-Estimation KRLS Diagnostics

from qqkrls import krls_residual_diagnostics, print_krls_diagnostics

diag = krls_residual_diagnostics(fit)
print_krls_diagnostics(diag)

Reports: R², Adjusted R², Effective df, AIC, BIC, Durbin-Watson, MAE, RMSE, Jarque-Bera.

Individual Diagnostic Tests

from qqkrls import bds_test, parameter_stability_test, jarque_bera

# BDS test for nonlinear dependence
bds = bds_test(series, max_dim=6)
# Returns: {'dimensions': [2..6], 'z_stats': [...], 'p_values': [...]}

# Andrews parameter stability test
stab = parameter_stability_test(series, trim=0.15)
# Returns: {'max_f': ..., 'exp_f': ..., 'ave_f': ...}

# Jarque-Bera normality test
jb = jarque_bera(series)
# Returns: {'statistic': ..., 'p_value': ...}

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Table & Export Functions

from qqkrls import (
    qqkrls_coefficient_table,
    krls_summary_table,
    descriptive_statistics,
    export_results_csv,
)

# LaTeX coefficient table with significance stars
latex = qqkrls_coefficient_table(result, digits=3, show_stars=True)

# LaTeX table of KRLS average marginal effects
latex = krls_summary_table(fit, digits=4)

# Descriptive statistics table (Mean, Median, Std, Skew, Kurt, JB)
latex = descriptive_statistics(df, caption="Descriptive Statistics")

# CSV export
export_results_csv(result, "qqkrls_results.csv", digits=4)

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Complete Workflow Guide

This section demonstrates the full research workflow from data loading to publication-ready output, mirroring the methodology in Adebayo et al. (2024).

Step 1: Data Preparation & Descriptive Statistics

import numpy as np
import pandas as pd
from qqkrls import descriptive_statistics

# Load your data
df = pd.read_csv("your_data.csv")
y = df["dependent_var"].values
X = df[["x1", "x2", "x3"]].values

# Generate descriptive statistics LaTeX table
latex = descriptive_statistics(df[["dependent_var", "x1", "x2", "x3"]])
print(latex)

Step 2: Pre-Estimation Diagnostics

Before applying KRLS/QQKRLS, demonstrate that the data exhibits nonlinearity:

from qqkrls import linearity_test_battery, print_diagnostics

diag = linearity_test_battery(y, X, col_names=["x1", "x2", "x3"])
print_diagnostics(diag)

# If RESET test rejects linearity → KRLS is justified
# If BDS test rejects i.i.d. → nonparametric approach is justified
# If Breusch-Pagan rejects homoskedasticity → quantile approach is justified

Step 3: KRLS Estimation

from qqkrls import krls, plot_krls_derivatives, plot_krls_fit, plot_krls_panel
from qqkrls import krls_residual_diagnostics, print_krls_diagnostics, krls_summary_table

# Fit KRLS (full nonparametric regression)
fit = krls(X, y, col_names=["x1", "x2", "x3"])

# Post-estimation diagnostics
diag = krls_residual_diagnostics(fit)
print_krls_diagnostics(diag)

# Visualize all marginal effects
plot_krls_panel(fit, save_path="krls_marginal_effects.png")

# Fitted vs actual
plot_krls_fit(fit, save_path="krls_fitted_actual.png")

# LaTeX table
print(krls_summary_table(fit))

Step 4: QQKRLS Estimation

from qqkrls import qqkrls, plot_qqkrls_heatmap, plot_qqkrls_3d
from qqkrls import plot_qqkrls_contour, plot_qqkrls_pvalue

# Define quantile grids
taus = np.arange(0.05, 1.0, 0.05)   # 19 quantiles
thetas = np.arange(0.05, 1.0, 0.05)

# Run QQKRLS for a single X variable
result = qqkrls(y, X[:, 0], y_quantiles=taus, x_quantiles=thetas,
                n_boot=200, verbose=True)

# Summary
result.summary()

# Heatmap (paper-style with significance stars)
plot_qqkrls_heatmap(result, title="QQKRLS: x1 → Y",
                    colorscale="paper", show_stars=True,
                    save_path="qqkrls_heatmap_x1.png")

# 3D surface
plot_qqkrls_3d(result, title="QQKRLS Surface: x1 → Y",
               save_path="qqkrls_3d_x1.png")

# Contour plot
plot_qqkrls_contour(result, save_path="qqkrls_contour_x1.png")

# P-value heatmap
plot_qqkrls_pvalue(result, save_path="qqkrls_pvalue_x1.png")

Step 5: Multi-Variable QQKRLS

from qqkrls import multi_qqkrls, plot_qqkrls_panel

# Run QQKRLS for all independent variables
results_dict = multi_qqkrls(
    y, X,
    col_names=["x1", "x2", "x3"],
    n_boot=200,
    verbose=True,
)

# Panel of heatmaps (one per variable)
plot_qqkrls_panel(results_dict, dep_var="Y",
                  save_path="qqkrls_panel.png")

Step 6: Visualization

# Customize any plot
fig, ax = plot_qqkrls_heatmap(
    result,
    title="Custom Title",
    colorscale="paper",
    show_stars=True,
    show_values=True,    # Show coefficient values in cells
    x_label=r"Quantiles of Energy ($\theta$)",
    y_label=r"Quantiles of CO$_2$ ($\tau$)",
    figsize=(14, 10),
    vmin=-0.5,
    vmax=0.5,
    star_fontsize=7,
    dpi=400,
    save_path="custom_heatmap.png",
)

Step 7: Export & Reporting

# CSV export
result.export_csv("results.csv", digits=4)

# LaTeX table with significance stars
latex = result.export_latex(
    value="coefficient",
    caption="QQKRLS Marginal Effects: Energy → CO₂",
    show_stars=True,
)
print(latex)

# Copy directly to your LaTeX document
with open("table_qqkrls.tex", "w") as f:
    f.write(latex)

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Ported From

This library is a faithful Python port of:

Source	Version	Authors
R CRAN KRLS	v1.0-0	Hainmueller & Hazlett
R CRAN QuantileOnQuantile	v1.0.3	Roudane
Python wqr	v1.0.1	Roudane

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References

1. Hainmueller, J. & Hazlett, C. (2014). Kernel Regularized Least Squares. Political Analysis, 22(2), 143-168. doi:10.1093/pan/mpt024

2. Sim, N. & Zhou, H. (2015). Oil Prices, US Stock Return, and the Dependence Between Their Quantiles. Journal of Banking & Finance, 55, 1-12. doi:10.1016/j.jbankfin.2015.01.013

3. Adebayo, T.S., Ozkan, O. & Eweade, B.S. (2024). Do energy efficiency R&D investments and ICT promote environmental sustainability in Sweden? A QQKRLS investigation. Journal of Cleaner Production, 440, 140832. doi:10.1016/j.jclepro.2024.140832

4. Adebayo, T.S., Meo, M.S., Eweade, B.S. & Ozkan, O. (2024). Analyzing the effects of solar energy innovations, digitalization, and economic globalization on environmental quality in the United States. Clean Technologies and Environmental Policy, 26, 4157-4176. doi:10.1007/s10098-024-02831-0

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Author

Dr. Merwan Roudane

• Email: [e-mail]
• GitHub: github.com/merwanroudane/qqkrls

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License

MIT License. See LICENSE for details.